HunterAgent combines LLM hypothesis generation with symbolic verification and cost-bounded graph search to reconstruct attack paths under anti-forensics, reporting 86.1% mean F1 on benchmarks with reduced hallucinations.
Llm-driven provenance forensics for threat investigation and detection
2 Pith papers cite this work. Polarity classification is still indexing.
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No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
citing papers explorer
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HunterAgent: Neuro-Symbolic Attack Trace Reconstruction under Anti-Forensics
HunterAgent combines LLM hypothesis generation with symbolic verification and cost-bounded graph search to reconstruct attack paths under anti-forensics, reporting 86.1% mean F1 on benchmarks with reduced hallucinations.
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Security Considerations for Multi-agent Systems
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.